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Cerebral Hacks

Knowledge & Automation

Turn documents, internal knowledge, and business processes into intelligent workflows — retrieval systems that give the right answer, sourced and trustworthy, not just a plausible one.

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Knowledge Architecture Foundation

A search bar isn't a knowledge system. We engineer grounded retrieval engines.

Keyword search fails on complex enterprise data. We engineer knowledge systems that parse multimodal files, enforce strict role-based access, and deliver verified, source-cited answers with sub-second retrieval latency.

Hybrid Retrieval

Semantic + Lexical Search

Combining dense vector embeddings with BM25 keyword matching for high-precision retrieval across domain terms.

How we engineer it: We index content in Qdrant with custom chunking boundaries and apply Cohere cross-encoder rerankers before context generation.

QdrantCohere RerankBM25
Document Ingestion

Multimodal Document Parsing

Ingesting complex PDFs, multi-column reports, spreadsheets, and scanned forms into structured schemas.

How we engineer it: We employ layout-aware document chunking, optical character recognition (OCR), and Pydantic validation for structured data.

Unstructured.ioOCRPydantic
Security & RBAC

Permission-Aware Querying

Strict tenant and role-based access control applied directly at the vector retrieval layer.

How we engineer it: We enforce PostgreSQL Row-Level Security (RLS) and metadata access control filters at query time to prevent data leaks.

PostgreSQL RLSACL FiltersJWT
Source Grounding

Direct Citation Verification

Every generated answer is backed by direct source page references and confidence scoring.

How we engineer it: We bind inline citation coordinates and enforce confidence threshold boundaries to eliminate hallucinated responses.

Citation EngineConfidence GatesMarkdown
Knowledge Architecture

How We Architect Knowledge & Automation Systems

From semantic vector search and multimodal document parsing to permission-bounded knowledge copilots.

Vector Retrieval

Hybrid Semantic Search

Multi-stage vector and keyword search that matches conceptual intent, skills, and technical queries beyond rigid text matching.

Dense vector indexing combined with BM25 keyword matching
Cross-encoder reranking (Cohere) for high relevance precision
Dynamic metadata filtering by date, tenant, and document type
QdrantCohere RerankFastAPI
Knowledge Copilot

Enterprise Knowledge Copilots

Grounded conversational assistants answering operational and technical questions directly from verified company documents.

Dynamic context window assembly with citation tracking
Strict grounding boundaries to eliminate hallucinations
Multi-turn conversational memory and query refinement
LangGraphClaude 3.5 SonnetPostgreSQL
Document Parsing

Document Intelligence & OCR

Multimodal ingestion pipeline that converts complex PDFs, tables, forms, and scanned assets into structured, queryable schemas.

Layout-aware chunking preserving tables and visual hierarchy
Automated optical character recognition (OCR) fallback
Pydantic-validated JSON extraction from raw documents
Unstructured.ioPydanticPython
RBAC Boundaries

Permission-Aware Retrieval

Security-first retrieval architectures that strictly enforce user authorization and multi-tenant document isolation.

Row-level security (RLS) enforcement at vector query time
Tenant and department access control list (ACL) filtering
Audit logging for every retrieved document chunk
PostgreSQL RLSQdrant ACLsFastAPI
Document Automation

Document-Triggered Workflows

Automated business workflows triggered upon document ingestion — extracting key fields and executing downstream updates.

Event-driven webhook ingestion for inbound files
Automated validation against business logic rules
Human review routing for low-confidence extractions
TemporalFastAPIRedis
Source Verification

Source-Cited Answering

Deterministic citation engine providing direct source page references, highlighted text snippets, and confidence scores.

Inline citation markers with source page numbers
Confidence threshold gates before generating answers
Explicit 'I do not have enough context' fallback responses
QdrantPythonReact Markdown

The 5-Stage Retrieval Lifecycle

Engineering knowledge architectures with strict chunking precision, hallucination barriers, and access controls.

01Understand

Map knowledge & access

Audit document types, data silos, user roles, and precision requirements.

02Design

Chunking & search topology

Design embedding models, layout-aware chunking, and index schemas.

03Build

Connect storage & vector DB

Connect PostgreSQL, Qdrant vector database, and FastAPI retrieval endpoints.

04Evaluate

Test precision & grounding

Evaluate context recall, hallucination boundaries, and citation accuracy.

05Operate

Monitor index & latency

Trace embedding latency, manage index drift, and optimize retrieval cost.

Frequently Asked Questions

Common questions about our enterprise retrieval architecture, document processing, and data security.

We design retrieval-augmented generation (RAG) pipelines with strict context boundaries, cross-encoder reranking, and deterministic confidence thresholds that explicitly reject ungrounded queries rather than fabricating answers.

Make your organization's knowledge usable.

Tell us what documents, data silos, or manual processes your team struggles with. We'll help design a secure, grounded retrieval architecture.

Talk to our engineersSee how we built Qurofy